Results (PhD Chapter 2)

This series of files compile all analyses done during Chapter 2:

All analyses have been done with R 3.6.2.

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Human activities considered for the analyses:

Data is also available for the number of captured individuals for dogwhelk (Buccinum sp.), common crab (Cancer irroratus), snowcrab (Chinoecetes opilio), nordic shrimp (Pandalus borealis), arctic surfclam (Mactromeris polynyma) and american lobster (Homarus americanus) fisheries.

1. Maps

1.1. General map

1.2. Parameters maps

Depth

Isobaths

Slope

2. Modelling of human activities influence

The influence of each human activity has been modelled at each station, in order to be later used in prediction models (see section 2).

We calculated an index of influence for each activity \(I_{ij}\), with an index of exposure \(E_{ij}\) and a specific weighting parameter \(w_{j}\).

\[ I_{ij} = w_{j} . E_{ij} \]

Two categories of exposure indices \(E_{ij}\) were defined and calculated differentely: one for fisheries and one for non-fishery human activities.

2.1. Calculating the indices of exposure

This corresponds to \(E_{ij}\) in Formula 1.

2.1.1. Non-fishery human activities

Here, \(E_{ij}\) have been calculated with the distance from the source(s) of the activity, the bathymetry and the hydrodynamical constraints.

\[ E_{ij} = f_{S} \left( D_{ij}, Z_{i}, H_{i} \right) . S_{j} + f_{M} \left( D_{ij}, Z_{i}, H_{i} \right) . M_{j} + f_{L} \left( D_{ij}, Z_{i}, H_{i} \right) . L_{j} \]

  • \(i\) is a station
  • \(j\) is a human activity
  • \(f_{S}\), \(f_{M}\), \(f_{L}\) are the resistance functions for small, medium and large particles, respectively
  • \(D_{ij}\) is the distance of station \(i\) from the source of activity \(j\)
  • \(Z_{i}\) is the bathymetry at station \(i\)
  • \(H_{i}\) is the hydrodynamic constraint at station \(i\)
  • \(S_{j}\), \(M_{j}\), \(L_{j}\) are the proportions of small, medium and large particles, respectively, for activity \(j\)

This step is done with the package gdistance. The aim of is to model the exposure of the station for each human activity. The exposure will be modelled as particles that can difuse in a certain area. We will establish a connectivity matrix between each cell of the raster, in order to calculate the least-cost path of the particles from the source of an activity to each station.

The connectivity matrix will be based on the “resistance seascape” concept. We will use the bathymetry and the hydrographic constraints to give a cost for the inclusion of a cell to the final path returned. This method will allow to take coasts and islands into consideration, which is necessary at BSI. Two underlying principles are established for this analysis:

  • stations spatially close at a similar depth are more susceptible to respond identically to a human activity than stations at different depths (proximity effect)
  • particles will disperse easily from shallow to deeper depths, while the reverse will be difficult (gravity effect)

This method will be applied to three types of particles, because each activity will not have the same type of diffusion in their environment. Small (S), medium (M) and large (L) theoretical particules will be considered here, and each will have its own resistance function \(f_{S}\), \(f_{M}\) and \(f_{L}\) (based on \(D_{ij}\), \(Z_{i}\) and \(H_{i}\)).

Particle Transition function
Small \(f_{S}\) = ifelse(d[1] > d[2], 1, 0.95)
Medium \(f_{M}\) = ifelse(d[1] > d[2], 1, 0.5)
Large \(f_{L}\) = ifelse(d[1] > d[2], 1, 0.1)

To calculate each \(E_{ij}\), we will sum these three components multiplied by the parameters \(S_{j}\), \(M_{j}\) and \(L_{j}\). These are the proportions of small, medium and large particules involved in the modeling of activity \(j\)’s effect, and they will be defined by us (some literature rewiew will be needed for groundtruthing.)

The following maps present the values of \(E_{ij}\) calculated for each non-fishery activity (grey = low exposure; dark blue = high exposure).

CityInf

InduInf

DredColl

DredDump

MoorSite

RainSew

WastSew

CityWha

InduWha

2.1.2. Fisheries

Here, \(E_{ij}\) have been calculated thanks to the database of David Beauchesne.

TO BE ADDED.

2.2. Setting the weighting parameters

This corresponds to \(w_{j}\) in Formula 1.

This table shows the weights \(w_{j}\) for each non-fishery human activity:

CityInf InduInf DredColl DredDump MoorSite RainSew WastSew CityWha InduWha
1 1 1 1 1 1 1 1 1

And this one shows the weights \(w_{j}\) for each fishery:

FishTrap FishTraw FishLine FishNet FishDred
1 1 1 1 1

2.3. Calculation of the index of cumulative influence

This corresponds to \(I_{ij}\) in Formula 1.

We can combine each \(I_{ij}\) thanks to the previous calculations. We will sum them to obtain a cumulative influence index:

\[ CI_{i} = \sum_{j} {I_{ij}} \]

Raw CI

Coloured CI

The cumulative influence index has been standardized, and represented with 0.1 breaks between each class (indigo = low influence; crimson = high influence).

Coloured CI + sources

The cumulative influence index has been standardized, and represented with 0.1 breaks between each class (indigo = low influence, less than 20%; crimson = high influence, higher than 80%).

Here are the scores for each sampled station:

These scores will be used for the HMSC models (see section 2).

Elliot Dreujou

2020-02-07